{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# &#128253; Intro Of This Notebook:\n",
    "\n",
    "Today's Progress : Today I've spent many hours exploring on Decision tree classification algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Lets Start!✌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "import math "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the dataset into the Training set and Test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\harun\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\harun\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Decision Tree Classification to the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.pyplot.xkcd.<locals>.dummy_ctx at 0x85f1ad0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classification (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.xkcd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Test set results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.pyplot.xkcd.<locals>.dummy_ctx at 0xaa02610>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classification (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.xkcd()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
